Evidence map›Paper›PMID 29070020›Full record

ArticleBMC public health2017

Schistosomiasis mansoni incidence data in Rwanda can improve prevalence assessments, by providing high-resolution hotspot and risk factors identification.

E Nyandwi, A Veldkamp, S Amer, C Karema, I Umulisa

Abstract read
In one paragraph

Article in BMC public health, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
  2. Impact of Climate Change on Schistosomiasis Transmission and Distribution-Scoping Review.International journal of environmental research and public health · 2025
    Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Molecular Tools and Schistosomiasis Transmission Elimination.The American journal of tropical medicine and hygiene · 2020
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

E NyandwiFaculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P. O. Box 217, 7500 AE, Enschede, the Netherlands. e.nyandwi@utwente.nl.
A VeldkampFaculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P. O. Box 217, 7500 AE, Enschede, the Netherlands.
S AmerFaculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P. O. Box 217, 7500 AE, Enschede, the Netherlands.
C KaremaSwiss Tropical and Public Health Institute, Socinstrasse 57, P.O. Box, CH-4002, Basel, Switzerland.
I UmulisaRwanda Biomedical Centre/ Malaria and Other Parasitic Diseases Division, P. O. Box 2514, Kigali, Rwanda.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSchistosomiasis mansoni constitutes a significant public health problem in Rwanda. The nationwide prevalence mapping conducted in 2007-2008 revealed that prevalence per district ranges from 0 to 69.5% among school children. In response, mass drug administration campaigns were initiated. However, a few years later some additional small-scale studies revealed the existence of areas of high transmission in districts formerly classified as low endemic suggesting the need for a more accurate methodology for identification of hotspots. This study investigated if confirmed cases of schistosomiasis recorded at health facility level can be used to, next to existing prevalence data, detect geographically more accurate hotspots of the disease and its associated risk factors.

methodsA GIS-based spatial and statistical analysis was carried out. Confirmed cases, recorded at primary health facilities level, were combined with demographic data to calculate incidence rates for each of 367 health facility service area. Empirical Bayesian smoothing was used to deal with rate instability. Incidence rates were compared with prevalence data to identify their level of agreement. Spatial autocorrelation of the incidence rates was analyzed using Moran's Index, to check if spatial clustering occurs. Finally, the spatial relationship between schistosomiasis distribution and potential risk factors was assessed using multiple regression.

resultsIncidence rates for 2007-2008 were highly correlated with prevalence values (R

conclusionIn Rwanda the high density of health facilities and the standardized microscopic laboratory diagnostic allow the derived data to be used to complement prevalence studies to identify hotspots of schistosomiasis and its associated risk factors. This type of information, in turn, can support disease control interventions and monitoring.

Indexed as

Bayes TheoremChildHealth FacilitiesHumansIncidencePopulation SurveillancePrevalenceReproducibility of ResultsRisk FactorsRwandaSchistosomiasis mansoniSpatial AnalysisEmpirical modelIncidence ratesRisk factorsSchistosomiasis mansoniSpatial scale

Identifiers

PMID29070020
PMCPMC5655984

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.